Agency task: Agency decisions
Model: Agency decisions by VoC
## Mixed Model Anova Table (Type 3 tests, LRT-method)
##
## Model: stage_1_choice ~ age_z * voc_z * condition_trial + (voc_z * condition_trial ||
## Model: subject_id)
## Data: agency_model_data
## Df full model: 12
## Effect df Chisq p.value
## 1 age_z 1 0.04 .840
## 2 voc_z 1 168.42 *** <.001
## 3 condition_trial 1 1.52 .217
## 4 age_z:voc_z 1 10.92 *** <.001
## 5 age_z:condition_trial 1 0.14 .712
## 6 voc_z:condition_trial 1 48.77 *** <.001
## 7 age_z:voc_z:condition_trial 1 5.31 * .021
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: stage_1_choice ~ age_z * voc_z * condition_trial + (1 + re1.voc_z +
## re1.condition_trial + re1.voc_z_by_condition_trial || subject_id)
## Data: data
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
##
## AIC BIC logLik deviance df.resid
## 38178.8 38282.6 -19077.4 38154.8 42082
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -28.0229 -0.4794 0.2265 0.5457 26.6942
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject_id (Intercept) 2.29016 1.5133
## subject_id.1 re1.voc_z 0.48884 0.6992
## subject_id.2 re1.condition_trial 0.64461 0.8029
## subject_id.3 re1.voc_z_by_condition_trial 0.05051 0.2247
## Number of obs: 42094, groups: subject_id, 135
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.94898 0.13154 7.214 5.42e-13 ***
## age_z -0.02665 0.13149 -0.203 0.839393
## voc_z 1.15265 0.06284 18.343 < 2e-16 ***
## condition_trial -0.08828 0.07109 -1.242 0.214347
## age_z:voc_z 0.21148 0.06282 3.366 0.000762 ***
## age_z:condition_trial -0.02626 0.07109 -0.369 0.711856
## voc_z:condition_trial 0.19899 0.02570 7.744 9.63e-15 ***
## age_z:voc_z:condition_trial 0.05987 0.02574 2.326 0.020032 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) age_z voc_z cndtn_ ag_z:v_ ag_z:c_ vc_z:_
## age_z 0.001
## voc_z 0.010 0.001
## conditn_trl 0.006 -0.002 0.002
## age_z:voc_z 0.001 0.009 0.007 0.000
## ag_z:cndtn_ -0.003 0.002 0.000 -0.001 0.001
## vc_z:cndtn_ 0.003 0.000 0.029 0.030 -0.001 0.004
## ag_z:vc_z:_ 0.000 0.001 0.001 0.004 0.023 0.030 0.024
Plot: Sensitivity to the value of choice

Plot: Sensitivity to value of choice with continuous age


Summary stats: Sensitivity to value of control
Agency task: Machine selection
Model: Optimal machine choices across trials by condition and
age
## Mixed Model Anova Table (Type 3 tests, LRT-method)
##
## Model: stage_2_acc ~ age_z * context * condition_trial + (context *
## Model: condition_trial || subject_id)
## Data: machine_model_data
## Df full model: 12
## Effect df Chisq p.value
## 1 age_z 1 18.88 *** <.001
## 2 context 1 25.05 *** <.001
## 3 condition_trial 1 66.49 *** <.001
## 4 age_z:context 1 0.26 .612
## 5 age_z:condition_trial 1 1.22 .268
## 6 context:condition_trial 1 3.44 + .064
## 7 age_z:context:condition_trial 1 1.99 .159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: stage_2_acc ~ age_z * context * condition_trial + (1 + re1.context1 +
## re1.condition_trial + re1.context1_by_condition_trial || subject_id)
## Data: data
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
##
## AIC BIC logLik deviance df.resid
## 13066.7 13160.8 -6521.4 13042.7 18640
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.0512 0.0954 0.2009 0.3937 2.4327
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject_id (Intercept) 1.7436 1.3205
## subject_id.1 re1.context1 0.5702 0.7551
## subject_id.2 re1.condition_trial 0.3025 0.5500
## subject_id.3 re1.context1_by_condition_trial 0.1182 0.3439
## Number of obs: 18652, groups: subject_id, 135
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.32963 0.12060 19.317 < 2e-16 ***
## age_z 0.53498 0.11947 4.478 7.54e-06 ***
## context1 0.38956 0.07338 5.309 1.10e-07 ***
## condition_trial 0.52529 0.05755 9.128 < 2e-16 ***
## age_z:context1 -0.03839 0.07341 -0.523 0.6010
## age_z:condition_trial 0.06404 0.05722 1.119 0.2631
## context1:condition_trial 0.08002 0.04225 1.894 0.0582 .
## age_z:context1:condition_trial -0.06096 0.04231 -1.441 0.1497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) age_z cntxt1 cndtn_ ag_z:1 ag_z:_ cnt1:_
## age_z 0.049
## context1 0.032 0.007
## conditn_trl 0.080 0.016 0.027
## ag_z:cntxt1 0.004 0.023 0.073 -0.001
## ag_z:cndtn_ 0.011 0.065 -0.001 0.089 0.020
## cntxt1:cnd_ 0.023 0.001 0.113 0.070 0.024 0.012
## ag_z:cnt1:_ -0.003 0.016 0.022 0.011 0.116 0.062 0.135
Plot: Proportion optimal machine selections across age groups and
trials

Explicit reward knowledge task
Explicit reward knowledge task: summary stats
Model: Explicit reward knowledge by age and true probabilities
## Mixed Model Anova Table (Type 3 tests, S-method)
##
## Model: error ~ zTrueProb * zAge + (1 | subject_id)
## Data: explicitKnow.filtered
## Effect df F p.value
## 1 zTrueProb 1, 673.00 22.78 *** <.001
## 2 zAge 1, 133.00 7.01 ** .009
## 3 zTrueProb:zAge 1, 673.00 0.26 .609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: error ~ zTrueProb * zAge + (1 | subject_id)
## Data: data
##
## REML criterion at convergence: 2763.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5472 -0.7030 -0.1787 0.4401 4.2139
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject_id (Intercept) 0.1176 0.3429
## Residual 1.6465 1.2831
## Number of obs: 810, groups: subject_id, 135
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.56296 0.05389 133.00000 29.005 < 2e-16 ***
## zTrueProb -0.21530 0.04511 673.00000 -4.772 2.23e-06 ***
## zAge -0.14272 0.05392 133.00000 -2.647 0.00911 **
## zTrueProb:zAge -0.02307 0.04514 673.00000 -0.511 0.60947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) zTrPrb zAge
## zTrueProb 0.000
## zAge 0.000 0.000
## zTruPrb:zAg 0.000 0.000 0.000
Plot: Explicit reward knowledge

---
title: "E2 VoC Analyses Part 2: Regression Analyses"
date: 1/8/24
output:
    html_document:
        df_print: 'paged'
        toc: true
        toc_float:
            collapsed: false
            smooth_scroll: true
        number_sections: false
        code_download: true
        self_contained: true
---

```{r chunk settings, include = FALSE}
# set chunk settings
knitr::opts_chunk$set(echo = FALSE, 
                      cache = TRUE,
                      message = FALSE,
                      warning = FALSE)
knitr::opts_chunk$set(dpi=600)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
```

```{r load libraries, include = F}

#load libraries
library(tidyverse)
library(glue)
library(afex)

#load scripts
source('analysis_scripts/voc_functions.R')
```

```{r import data}

# read in learning data
learning_data <- read_csv('data/processed/learning_data.csv')

# read in participant ages
participant_ages <- read_csv('data/voc_sub_info.csv') 

# join
learning_data <- inner_join(learning_data, participant_ages, by = c('subject_id')) %>%
  mutate(age_group = case_when(age < 13 ~ 'Children',
                               age < 18 & age > 12.99 ~ 'Adolescents',
                               age > 18 ~ 'Adults'))

learning_data$age_group <- factor(learning_data$age_group,
                                  levels = c("Children", "Adolescents", "Adults"))

```

```{r process learning data}
learning_data <- learning_data %>%
  mutate(ev_choice = case_when(context == 0 ~ 9,
                               context == 1 ~ 7,
                               context == 2 ~ 5),
         ev_comp = 5 + offer,
         voc = ev_choice - ev_comp,
         better_machine = case_when(reward_prob_L > reward_prob_R ~ 1,
                                    reward_prob_L < reward_prob_R ~ 0,
         ),
         stage_2_acc = case_when(stage_2_choice == better_machine ~ 1,
                                 stage_2_choice != better_machine ~ 0)) %>%
  group_by(subject_id, context) %>%
  mutate(condition_trial = rank(trial),
         block = floor((trial-1)/21 + 1))

# exclude first-stage misses and first-stage RT < 150 ms
learning_data_filtered <- learning_data %>%
  filter(stage_1_rt > 150)

#exclude participants who made more than 300 of the same agency decisions
stage1_decisions <- learning_data_filtered %>%
  group_by(subject_id) %>%
  summarize(agency_choices = sum(stage_1_choice == 1)) %>%
  filter(agency_choices < 299) %>%
  filter(agency_choices > 15)

#exclude from learning data
learning_data_filtered <- learning_data_filtered %>%
  filter(subject_id %in% stage1_decisions$subject_id)

```

# Participant info
```{r subject information}
sub_info <- learning_data_filtered %>%
  ungroup() %>%
  select(subject_id, age, age_group, gender) %>%
  unique() %>%
  group_by(age_group) %>%
  summarize(N = n(), 
            min_age = min(age, na.rm = T),
            max_age = max(age, na.rm = T),
            mean_age = mean(age, na.rm = T),
            sd_age = sd(age, na.rm = T),
            n_female = sum(gender == 'Female'),
            n_male = sum(gender == 'Male'),
            n_other = sum(gender == 'Other'))
sub_info

```


# Agency task: Agency decisions 
## Model: Agency decisions by VoC
```{r agency model}
# select relevant variables 
agency_model_data <- learning_data_filtered %>%
  select(subject_id, stage_1_choice, voc, condition_trial, block, trial, age, age_group)

## REGRESSION MODEL ##
#z score continuous variables
agency_model_data$subject_id <- factor(agency_model_data$subject_id)
agency_model_data$voc_z <- scale_this(agency_model_data$voc)
agency_model_data$condition_trial <- scale_this(agency_model_data$condition_trial)
agency_model_data$age_z <- scale_this(agency_model_data$age)

#run model
agency_model <- mixed(stage_1_choice ~ age_z * voc_z * condition_trial + (voc_z * condition_trial || subject_id),
                      data = agency_model_data,
                      family = "binomial",
                      method = "LRT",
                      expand_re = T,
                      control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=1e6)))

#show model results
agency_model
summary(agency_model)
```

## Plot: Sensitivity to the value of choice
```{r voc plot, fig.height = 4, fig.width = 7, unit = "in"}
## PLOT ##
agency_sub_means <- agency_model_data %>% 
  mutate(task_half = case_when(trial < 158 ~ "First Half of Task",
                              trial > 157 ~ "Second Half of Task")) %>%
  group_by(task_half, voc, subject_id, age_group) %>%
  summarize(mean_sub_agency = mean(stage_1_choice, na.rm = T))

agency_means <- agency_sub_means %>% 
  group_by(task_half, voc, age_group) %>%
  summarize(mean_agency = mean(mean_sub_agency, na.rm = T),
            se_agency = sd(mean_sub_agency / sqrt(n())))

agency_plot <- ggplot(agency_means, aes(x = voc, y = mean_agency, color = age_group)) +
  facet_wrap(~task_half) +
  geom_point(aes(color = age_group)) + 
  geom_errorbar(aes(ymin = mean_agency - se_agency, ymax = mean_agency + se_agency), width = .1) + 
  geom_line() +
  voc_theme() + 
  scale_color_manual(values=c("#84347C", "#B40424", "#EB6D1E"), name = "Age Group") +
  xlab("Value of Choice (VoC)") +
  ylab("Proportion Agency Choices") +
  geom_hline(yintercept = .5, linetype = "dashed") +
  geom_vline(xintercept = 0, linetype = "dashed")
agency_plot
```


## Plot: Sensitivity to value of choice with continuous age 
```{r voc plot continuous age, fig.height = 3.9, fig.width = 3, unit = "in"}

#run model without age to get random effects for each participant
agency_glmer <- mixed(stage_1_choice ~  voc_z * condition_trial + (voc_z * condition_trial | subject_id),
                      data = agency_model_data, 
                      family = binomial, 
                      method = "LRT",
                      control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=1e6)),
                      return = "merMod") 

#get fixed effect of zVoC
VoC_fixedeff <- as.data.frame(coef(summary(agency_glmer)))$Estimate[2]
VoC_int_fixedeff <- as.data.frame(coef(summary(agency_glmer)))$Estimate[4]

#get random effects
VoC_effects <- ranef(agency_glmer)$subject_id %>%
    rownames_to_column(var = "subject_id")

#combine with age
VoC_subEffects <- agency_model_data %>%
    select(subject_id, age) %>% 
    unique() %>%
    left_join(VoC_effects, by = c("subject_id")) %>%
    mutate(zVoCFull = voc_z + VoC_fixedeff, 
           intFull = `voc_z:condition_trial` + VoC_int_fixedeff)

#plot age by VoC effect
VoC_plot_continuousAge <- ggplot(VoC_subEffects, aes(x = age, y = zVoCFull)) +
    geom_point(color = "#EB6D1E") + 
    geom_smooth(method = "lm", color = "#84347C", fill = "#84347C") +
    voc_theme() + 
    xlab("Age") +
    ylab("VoC Effect") 
VoC_plot_continuousAge

#plot age by VoC x trial effect
VoC_plot_continuousAgeTrial <- ggplot(VoC_subEffects, aes(x = age, y = intFull)) +
    geom_point(color = "#EB6D1E") + 
    geom_smooth(method = "lm", color = "#84347C", fill = "#84347C") +
    voc_theme() + 
    xlab("Age") +
    ylab("VoC x Trial Effect") 
VoC_plot_continuousAgeTrial
```



## Summary stats: Sensitivity to value of control
```{r voc summary stats}

# What proportion of trials did participants choose agency when VoC was 0?
VoC_zero_means_sub <- learning_data_filtered %>% 
    filter(voc == 0) %>%
    group_by(subject_id, age_group) %>%
    summarize(meanSubAgency = mean(stage_1_choice, na.rm = T))

VoC_zero_means <- VoC_zero_means_sub %>%
  ungroup() %>%
  summarize(meanAgency = mean(meanSubAgency, na.rm = T),
              seAgency = sd(meanSubAgency / sqrt(n())))
VoC_zero_means
```



# Agency task: Machine selection
## Model: Optimal machine choices across trials by condition and age
```{r machine selection decisions}
# select variables for inclusion in mixed-effects model (no age for now)
machine_model_data <- learning_data_filtered %>%
  filter(stage_1_choice == 1) %>%
  filter(context < 2) %>%
  select(subject_id, stage_2_acc, context, condition_trial, block, age, age_group) %>%
  drop_na()

## REGRESSION MODEL ##
#z score continuous variables
machine_model_data$subject_id <- factor(machine_model_data$subject_id)
machine_model_data$context <- factor(machine_model_data$context)
machine_model_data$condition_trial <- scale_this(machine_model_data$condition_trial)
machine_model_data$age_z <- scale_this(machine_model_data$age)

#run model
machine_model <- mixed(stage_2_acc ~ age_z * context * condition_trial + (context * condition_trial || subject_id),
                      data = machine_model_data,
                      family = "binomial",
                      method = "LRT",
                      expand_re = T,
                      control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=1e6)))

#show model results
machine_model
summary(machine_model)
```

## Plot: Proportion optimal machine selections across age groups and trials
```{r plot bandit choices across trials, width = 7, height = 4, unit = "in"}

## PLOT ##
machine_selection_sub_means <- machine_model_data %>%
  group_by(context, block, subject_id, age_group) %>% 
  summarize(sub_acc = mean(stage_2_acc, na.rm = T))

machine_selection_means <- machine_selection_sub_means %>%
  group_by(context, block, age_group) %>% 
  summarize(mean_acc = mean(sub_acc),
            se = sd(sub_acc)/sqrt(n()))

machine_selection_plot <- ggplot(machine_selection_means, aes(x=block, y=mean_acc, color=factor(context))) +
  facet_wrap(~age_group) +
  geom_point(size = 3) +
  geom_jitter(data = machine_selection_sub_means,  aes(x=block, y=sub_acc, color=factor(context)), size = .5) +
  geom_smooth(method = "lm", aes(fill = factor(context))) +
  geom_hline(yintercept = .5, linetype="dashed") +
  ylab("Proportion Optimal Machine Selections") +
  xlab("Block") +
  scale_x_continuous(breaks = c(4, 8, 12)) +
  scale_fill_manual(name="Context",
                    labels=c("90/10",
                             "70/30"),
                    values=c(color1, color3), 
                    guide = guide_legend(reverse=TRUE)) +
  scale_color_manual(name="Context",
                     labels=c("90/10",
                              "70/30"),
                     values=c(color1, color3),
                     guide = guide_legend(reverse=TRUE)) +
  voc_theme() +
  theme(strip.text = element_text(size=12))
machine_selection_plot
```



# Explicit reward knowledge task 
## Explicit reward knowledge task: summary stats
```{r explicit knowledge task}

# Read in data
explicitKnow <- read_csv('data/processed/explicit_data.csv') %>%
    filter(subject_id %in% stage1_decisions$subject_id)

#combine with age
explicitKnow <- full_join(explicitKnow, participant_ages, by = c("subject_id")) 

explicitKnow %>% 
  group_by(subject_id, age) %>% 
  summarize(m = mean(error)) %>% 
  ungroup() %>% 
  summarize(meanErr = mean(m, na.rm=T), sd = sd(m, na.rm = T))
```

## Model: Explicit reward knowledge by age and true probabilities
```{r explicit knowledge model}

#re-scale age and zTrueProb
explicitKnow.filtered <- explicitKnow %>%
    select(subject_id, age, true_prob, error) %>%
    drop_na()

explicitKnow.filtered$zAge <- scale(explicitKnow.filtered$age)
explicitKnow.filtered$zTrueProb <- scale(explicitKnow.filtered$true_prob)

# run model
explicitKnow_errorbyTrueProbAge.mixed <- mixed(error ~ zTrueProb*zAge + (1|subject_id), 
                                               data = explicitKnow.filtered,
                                               method = "S") 
explicitKnow_errorbyTrueProbAge.mixed
summary(explicitKnow_errorbyTrueProbAge.mixed)
```

## Plot: Explicit reward knowledge
```{r plot explicit knowledge}

explicitKnow <- explicitKnow %>%
  mutate(age_group = case_when(age < 13 ~ 'Children',
                               age < 18 & age > 12.99 ~ 'Adolescents',
                               age > 18 ~ 'Adults'))

explicitKnow$age_group <- factor(explicitKnow$age_group,
                                  levels = c("Children", "Adolescents", "Adults"))

# plot response by bandit
explicitKnow %>%
  drop_na() %>%
    ggplot(., aes(x=factor(true_prob), y=response, fill=age_group)) +
    geom_boxplot() +
    scale_fill_manual(values = c(color1, color2, color3), name = "Age Group") +
    ylab("Reported Reward Probability") +
    xlab("True Reward Probability") +
    scale_x_discrete(labels = c("10%", "30%", "50%", "70%", "90%")) +
    scale_y_continuous(breaks = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), 
                     labels = c("10%", "20%", "30%", "40%", "50%", "60%", "70%", "80%", "90%", "100%")) +
    voc_theme()
```
